BACKGROUND:Myocardial perfusion imaging (MPI) results in downstream changes to medication prescription. While the benefits of medical therapy for coronary artery disease (CAD) are established, how this varies with MPI findings is unknown. Our goal was to evaluate the association of medical therapy with survival among patients undergoing MPI, including differential associations as a function of imaging findings. METHODS:Consecutive patients who underwent single-photon emission computed tomography MPI for suspected CAD between January 2015 and December 2021 were identified. Multivariable Cox regression modeling was used to assess the associations between medical therapy and all-cause mortality. RESULTS:In total, 7802 patients were included with a mean age of 66.1 ± 12.0 years and 3841 (49.2 %) male patients. Angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACE inhibitors/ARBs) were associated with lower mortality (adjusted hazard ratio [HR] .66, 95 % confidence interval [CI]: .57-.77, P < .001). Beta-blockers were not associated with mortality overall (adjusted HR .95, 95 % CI: .83-1.10, P = .506) but were associated with lower mortality among patients with more ischemia (HR .94 per summed difference score point, 95 % CI: .90-.97, P-value <.001). Statins were associated with greater survival in patients with coronary calcium (adjusted HR .67, 95 % CI .56-.81, P = <0.001) but not in patients without assessment of coronary calcium (adjusted HR: 1.16, 95 % CI: .91-1.49 P = .236). CONCLUSION:ACE/ARB prescription was significantly associated with improved survival. Beta-blocker prescription was associated with greater survival in patients with ischemia and statins were in patients with coronary calcification. Findings from MPI may identify patients more likely to benefit from specific therapies, suggesting a role for hybrid MPI in guiding medical therapy for CAD.
The benefits of regular physical exercise, primarily moderate-intensity exercise, are widely known, recognized, and acclaimed. As an important lifestyle modification, regular training activities are gaining increasing popularity in the general population. Apart from the obvious benefits, physical exercise may carry the risk of trauma, cardiovascular events, and exercise-induced asthma and bronchoconstriction, to name just a few well-known clinical situations reported in athletes, both recreational and competitive. In susceptible individuals, acute bouts of exercise may lead to the appearance of urticaria, angioedema, and anaphylaxis. Among these three clinical phenomena, angioedema is the least addressed and recognized, often being considered an accompanying clinical feature of urticaria or a hallmark of imminent anaphylactic reaction. To fill this knowledge gap, in this review, we focus on exercise-associated angioedema symptoms and highlight their most important features, both as isolated phenomena and in association with urticaria or anaphylaxis.
The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO2 (S-CO2) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO2 power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO2 CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results, a novel Improved Whale Optimization Algorithm and Bi-dictionary Long Short-Term Memory (IWOA-BiLSTM) algorithm model is established to predict CFB temperature, NOx emission concentration, and SO2 emission concentration, respectively. Results show that CO and SO2 decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx declines, and the change of SO2 is not obvious. Compared with two other basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.032 %, 0.231 %, and 0.157 %, respectively, which can meet the prediction requirements with acceptable accuracy.
In order to improve the combustion efficiency and decomposition rate of the cement calciner and reduce pollutant emission, a performance optimization method based on Computational Fluid Dynamics (CFD) numerical simulation integrated with machine learning is proposed. The Multiphase Particle-in-cell (MP-PIC) method and the chemical reaction models are employed to simulate the coal combustion and CaCO3 decomposition process, whose calculation results are combined with the industrial practical data of the cement plant, so as to establish a more comprehensive training database. On this basis, a novel Topology Particle Swarm Optimization algorithm integrating with Convolutional Neural Network and Long Short-Term Memory (RITPSO-CNN-LSTM) algorithm model is established to predict combustion efficiency, decomposition rate, and NOx emission, respectively. Results show that compared with two other relative basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.045%, 0.038%, and 0.021%, respectively. The addition of CFD simulation data makes the prediction model more applicable with higher stability and accuracy. Based on the prediction results, Grey Wolf Optimizer (GWO) algorithm is employed to optimize operating parameters, and finally the average optimization amount of combustion efficiency, decomposition rate, and NOx emission are 2.17%, 2.24%, and 6.15 ppm, respectively, which meet the optimization requirements.
